diff --git a/examples/pcsaft/parameter_adjustment/data/hexane_viscosity.csv b/examples/pcsaft/parameter_adjustment/data/hexane_viscosity.csv new file mode 100644 index 000000000..da39ea83b --- /dev/null +++ b/examples/pcsaft/parameter_adjustment/data/hexane_viscosity.csv @@ -0,0 +1,104 @@ +temperature / K,pressure / bar,viscosity / mPas,phase +450.0,0.1,0.009434,vapor +450.0,1.1,0.0094218,vapor +450.0,2.1,0.009433899999999999,vapor +450.0,3.1,0.0094759,vapor +450.0,4.1,0.0095515,vapor +450.0,5.1,0.00966,vapor +450.0,6.1,0.009798399999999999,vapor +450.0,7.1,0.0099619,vapor +450.0,8.1,0.010146,vapor +450.0,9.1,0.010348,vapor +450.0,10.1,0.010565,vapor +450.0,11.1,0.010798,vapor +450.0,12.1,0.011049999999999999,vapor +450.0,12.329,0.011111,vapor +450.0,12.329,0.082036,liquid +450.0,13.1,0.082329,liquid +450.0,14.1,0.082705,liquid +450.0,15.1,0.083076,liquid +450.0,16.1,0.08344299999999999,liquid +450.0,17.1,0.083805,liquid +450.0,18.1,0.084163,liquid +450.0,19.1,0.08451800000000001,liquid +450.0,20.099999999999998,0.084868,liquid +450.0,21.1,0.085215,liquid +450.0,22.1,0.085558,liquid +450.0,23.1,0.08589699999999999,liquid +450.0,24.1,0.086233,liquid +450.0,25.1,0.08656599999999999,liquid +450.0,26.1,0.086896,liquid +450.0,27.1,0.087223,liquid +450.0,28.1,0.08754699999999999,liquid +450.0,29.1,0.087868,liquid +450.0,30.1,0.088187,liquid +450.0,31.1,0.088502,liquid +450.0,32.1,0.08881499999999999,liquid +450.0,33.1,0.089126,liquid +450.0,34.1,0.089434,liquid +450.0,35.1,0.08974,liquid +450.0,36.1,0.090043,liquid +450.0,37.1,0.090344,liquid +450.0,38.1,0.090643,liquid +450.0,39.1,0.09094,liquid +450.0,40.1,0.091235,liquid +450.0,41.1,0.091528,liquid +450.0,42.1,0.091818,liquid +450.0,43.1,0.09210700000000001,liquid +450.0,44.1,0.09239399999999999,liquid +450.0,45.1,0.092679,liquid +450.0,46.1,0.09296199999999999,liquid +450.0,47.1,0.09324400000000001,liquid +450.0,48.1,0.093524,liquid +450.0,49.1,0.093802,liquid +450.0,50.1,0.094078,liquid +450.0,51.1,0.09435299999999999,liquid +450.0,52.1,0.094626,liquid +450.0,53.1,0.094898,liquid +450.0,54.1,0.09516799999999999,liquid +450.0,55.1,0.095437,liquid +450.0,56.1,0.095704,liquid +450.0,57.1,0.09597,liquid +450.0,58.1,0.096234,liquid +450.0,59.1,0.09649699999999999,liquid +450.0,60.1,0.096759,liquid +450.0,61.1,0.09702,liquid +450.0,62.1,0.097279,liquid +450.0,63.1,0.097537,liquid +450.0,64.1,0.097793,liquid +450.0,65.1,0.098049,liquid +450.0,66.1,0.098303,liquid +450.0,67.1,0.09855599999999999,liquid +450.0,68.1,0.098808,liquid +450.0,69.1,0.099059,liquid +450.0,70.1,0.09930800000000001,liquid +450.0,71.1,0.09955699999999999,liquid +450.0,72.1,0.09980399999999999,liquid +450.0,73.1,0.10005,liquid +450.0,74.1,0.1003,liquid +450.0,75.1,0.10053999999999999,liquid +450.0,76.1,0.10078,liquid +450.0,77.1,0.10103,liquid +450.0,78.1,0.10127,liquid +450.0,79.1,0.10150999999999999,liquid +450.0,80.1,0.10175000000000001,liquid +450.0,81.1,0.10199,liquid +450.0,82.10000000000001,0.10221999999999999,liquid +450.0,83.10000000000001,0.10246000000000001,liquid +450.0,84.1,0.1027,liquid +450.0,85.1,0.10293000000000001,liquid +450.0,86.1,0.10317,liquid +450.0,87.1,0.1034,liquid +450.0,88.1,0.10363,liquid +450.0,89.1,0.10386,liquid +450.0,90.1,0.10409,liquid +450.0,91.1,0.10432,liquid +450.0,92.1,0.10454999999999999,liquid +450.0,93.1,0.10478,liquid +450.0,94.1,0.10500999999999999,liquid +450.0,95.1,0.10524,liquid +450.0,96.1,0.10546,liquid +450.0,97.1,0.10568999999999999,liquid +450.0,98.1,0.10590999999999999,liquid +450.0,99.1,0.10614,liquid +450.0,100.1,0.10636,liquid diff --git a/examples/pcsaft/parameter_adjustment/generate_data.ipynb b/examples/pcsaft/parameter_adjustment/generate_data.ipynb deleted file mode 100644 index 4ac6fbc4c..000000000 --- a/examples/pcsaft/parameter_adjustment/generate_data.ipynb +++ /dev/null @@ -1,595 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "713c929f-ae71-42ec-aca8-efbb2d60ddcd", - "metadata": {}, - "source": [ - "# Generate vapor pressure data for hexane\n", - "\n", - "An helper-notebook to generate data for parameter adjustment example. Uses data for hexane from the [NIST WebBook](https://webbook.nist.gov/chemistry/fluid/) and applies artifical noise." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "8b78ad17-2f3a-4f62-ad4f-a0142a1d87f1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "sns.set_context('talk')\n", - "sns.set_palette('Dark2')\n", - "sns.set_style('ticks')" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "b0cc1fcc-89a3-45a1-a59c-10baa39b909e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Temperature (K)Pressure (bar)Density (l, kg/m3)Volume (l, m3/kg)Internal Energy (l, kJ/mol)Enthalpy (l, kJ/mol)Entropy (l, J/mol*K)Cv (l, J/mol*K)Cp (l, J/mol*K)Sound Spd. (l, m/s)...Volume (v, m3/kg)Internal Energy (v, kJ/mol)Enthalpy (v, kJ/mol)Entropy (v, J/mol*K)Cv (v, J/mol*K)Cp (v, J/mol*K)Sound Spd. (v, m/s)Joule-Thomson (v, K/bar)Viscosity (v, uPa*s)Therm. Cond. (v, W/m*K)
0250.00.015229697.230.001434-17.983-17.983-60.956138.32180.841301.5...15.80514.23416.30876.208116.89125.30160.458.21405.25330.010233
1251.00.016265696.370.001436-17.802-17.802-60.234138.55181.101296.7...14.85514.35016.43276.158117.23125.64160.748.12415.27450.010299
2252.00.017361695.510.001438-17.621-17.621-59.513138.77181.371292.0...13.97114.46716.55776.113117.57125.98161.038.03505.29570.010366
3253.00.018519694.640.001440-17.440-17.439-58.795139.00181.631287.2...13.14814.58416.68276.073117.91126.33161.317.94685.31680.010433
4254.00.019743693.780.001441-17.258-17.258-58.078139.23181.901282.5...12.38014.70116.80776.036118.25126.68161.607.85945.33800.010500
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5 rows × 25 columns

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" - ], - "text/plain": [ - " Temperature (K) Pressure (bar) Density (l, kg/m3) Volume (l, m3/kg) \\\n", - "0 250.0 0.015229 697.23 0.001434 \n", - "1 251.0 0.016265 696.37 0.001436 \n", - "2 252.0 0.017361 695.51 0.001438 \n", - "3 253.0 0.018519 694.64 0.001440 \n", - "4 254.0 0.019743 693.78 0.001441 \n", - "\n", - " Internal Energy (l, kJ/mol) Enthalpy (l, kJ/mol) Entropy (l, J/mol*K) \\\n", - "0 -17.983 -17.983 -60.956 \n", - "1 -17.802 -17.802 -60.234 \n", - "2 -17.621 -17.621 -59.513 \n", - "3 -17.440 -17.439 -58.795 \n", - "4 -17.258 -17.258 -58.078 \n", - "\n", - " Cv (l, J/mol*K) Cp (l, J/mol*K) Sound Spd. (l, m/s) ... \\\n", - "0 138.32 180.84 1301.5 ... \n", - "1 138.55 181.10 1296.7 ... \n", - "2 138.77 181.37 1292.0 ... \n", - "3 139.00 181.63 1287.2 ... \n", - "4 139.23 181.90 1282.5 ... \n", - "\n", - " Volume (v, m3/kg) Internal Energy (v, kJ/mol) Enthalpy (v, kJ/mol) \\\n", - "0 15.805 14.234 16.308 \n", - "1 14.855 14.350 16.432 \n", - "2 13.971 14.467 16.557 \n", - "3 13.148 14.584 16.682 \n", - "4 12.380 14.701 16.807 \n", - "\n", - " Entropy (v, J/mol*K) Cv (v, J/mol*K) Cp (v, J/mol*K) \\\n", - "0 76.208 116.89 125.30 \n", - "1 76.158 117.23 125.64 \n", - "2 76.113 117.57 125.98 \n", - "3 76.073 117.91 126.33 \n", - "4 76.036 118.25 126.68 \n", - "\n", - " Sound Spd. (v, m/s) Joule-Thomson (v, K/bar) Viscosity (v, uPa*s) \\\n", - "0 160.45 8.2140 5.2533 \n", - "1 160.74 8.1241 5.2745 \n", - "2 161.03 8.0350 5.2957 \n", - "3 161.31 7.9468 5.3168 \n", - "4 161.60 7.8594 5.3380 \n", - "\n", - " Therm. Cond. (v, W/m*K) \n", - "0 0.010233 \n", - "1 0.010299 \n", - "2 0.010366 \n", - "3 0.010433 \n", - "4 0.010500 \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Taken from NIST WebBook\n", - "data = pd.read_csv(\"data/vle_data.csv\", delimiter=\"\\t\")\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "0b8c507f-a905-4a0f-a151-577046800339", - "metadata": {}, - "outputs": [], - "source": [ - "n = len(data)\n", - "\n", - "# apply noise to data\n", - "percent_uncertainty = 0.05\n", - "data_gaussian = np.random.normal(data[\"Pressure (bar)\"], data[\"Pressure (bar)\"] * percent_uncertainty)\n", - "\n", - "# add outliers\n", - "percent_outliers = 0.1\n", - "for i in np.random.randint(0, n + 1, int(n * percent_outliers)):\n", - " data_gaussian[i] = np.random.normal(data_gaussian[i], data_gaussian[i] * 0.4)" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "7ccde5f2-71a0-458a-be40-4ffbcde05288", - "metadata": {}, - "outputs": [], - "source": [ - "psat_data = pd.DataFrame(\n", - " np.array([data[\"Temperature (K)\"].values, data_gaussian]).T, \n", - " columns=[\"temperature / K\", \"vapor_pressure / bar\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "b59fd76c-9af7-4fcd-890a-ba7a3c5cc3a7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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Temperature (K)Pressure (bar)Density (kg/m3)Volume (m3/kg)Internal Energy (kJ/mol)Enthalpy (kJ/mol)Entropy (J/mol*K)Cv (J/mol*K)Cp (J/mol*K)Sound Spd. (m/s)Joule-Thomson (K/bar)Viscosity (uPa*s)Therm. Cond. (W/m*K)Phase
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4350.07.0606.220.0016501.68251.78204.9069168.62216.20853.88-0.027424188.290.10596liquid
\n", - "
" - ], - "text/plain": [ - " Temperature (K) Pressure (bar) Density (kg/m3) Volume (m3/kg) \\\n", - "0 350.0 3.0 605.52 0.001651 \n", - "1 350.0 4.0 605.69 0.001651 \n", - "2 350.0 5.0 605.87 0.001651 \n", - "3 350.0 6.0 606.05 0.001650 \n", - "4 350.0 7.0 606.22 0.001650 \n", - "\n", - " Internal Energy (kJ/mol) Enthalpy (kJ/mol) Entropy (J/mol*K) \\\n", - "0 1.7158 1.7584 5.0021 \n", - "1 1.7074 1.7643 4.9782 \n", - "2 1.6991 1.7702 4.9544 \n", - "3 1.6908 1.7761 4.9306 \n", - "4 1.6825 1.7820 4.9069 \n", - "\n", - " Cv (J/mol*K) Cp (J/mol*K) Sound Spd. (m/s) Joule-Thomson (K/bar) \\\n", - "0 168.62 216.40 849.15 -0.027111 \n", - "1 168.62 216.35 850.34 -0.027190 \n", - "2 168.62 216.30 851.52 -0.027269 \n", - "3 168.62 216.25 852.70 -0.027347 \n", - "4 168.62 216.20 853.88 -0.027424 \n", - "\n", - " Viscosity (uPa*s) Therm. Cond. (W/m*K) Phase \n", - "0 187.21 0.10572 liquid \n", - "1 187.48 0.10578 liquid \n", - "2 187.75 0.10584 liquid \n", - "3 188.02 0.10590 liquid \n", - "4 188.29 0.10596 liquid " - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Taken from NIST WebBook\n", - "data = pd.read_csv(\"data/liquid_data.csv\", delimiter=\"\\t\")\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "c2f30a66-68f4-4d85-9853-114e2a183053", - "metadata": {}, - "outputs": [], - "source": [ - "n = len(data)\n", - "\n", - "# apply noise to data\n", - "percent_uncertainty = 0.01\n", - "data_gaussian = np.random.normal(data[\"Density (kg/m3)\"], data[\"Density (kg/m3)\"] * percent_uncertainty)\n", - "\n", - "# add outliers\n", - "percent_outliers = 0.1\n", - "for i in np.random.randint(0, n, int(n * percent_outliers)):\n", - " data_gaussian[i] = np.random.normal(data_gaussian[i], data_gaussian[i] * 0.03)\n", - " \n", - "rho_liq_data = pd.DataFrame(\n", - " np.array([data[\"Temperature (K)\"].values, data[\"Pressure (bar)\"].values, data_gaussian]).T, \n", - " columns=[\"temperature / K\", \"pressure / bar\", \"density / kg/m3\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "733881ad-51dd-4ab1-b943-765b06131335", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.scatterplot(data=rho_liq_data, y=\"temperature / K\", x=\"density / kg/m3\", marker=\"x\");\n", - "plt.xlabel(r\"$\\rho^{L}$ / kg / m³\")\n", - "plt.ylabel(r\"$T$ / K\");" - ] - }, - { - "cell_type": "markdown", - "id": "4e10738f-ae09-4f3e-a3f4-fcc8e861fdb8", - "metadata": {}, - "source": [ - "## Store data" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "aaabf4be-c09e-4173-9173-43922fbecf6a", - "metadata": {}, - "outputs": [], - "source": [ - "psat_data.to_csv('data/hexane_vapor_pressure.csv', index=False)\n", - "rho_liq_data.to_csv('data/hexane_liquid_density.csv', index=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}